Conventional environment recognition devices recognize objects such as a traveling lane of a subject vehicle, preceding vehicles on the traveling lane, and traffic signs on the basis of color images (monocular images) in front of the vehicle, imaged by an on-board camera of the subject vehicle (JP-6-348991A).
These devices split a three-dimensional space (luminance, brightness, saturation) representing colors into plural areas, and with the central value of each of the split areas as a color index, classify the colors of individual pixels constituting a color image by the color indexes. The devices perform area splitting processing that splits the image into areas by the classified colors, and perform knowledge processing that determines whether the positions and shapes of the areas obtained as a result of the area splitting processing match knowledge on objects provided in advance, thereby recognizing objects.
Another conventional method for identifying objects from a result of area splitting is a pattern matching.
However, when objects having the same color overlap one another in a color image, correct area splitting (shape acquisition) cannot be performed by the area splitting using image data. Thus, it is impossible to correctly determine to which object a particular area belongs by knowledge processing based on area shapes and pattern matching.
Since the area splitting processing is performed on a pixel basis, processing load on a computer is large, and particularly a method of identifying objects by pattern matching would cause enormous processing load.
Recently, to ease the searching for recorded images and the provision of information, there is a demand to extract objects contained in an image and store descriptions about the extracted objects in association with the image. To meet the demand, it is required to recognize the objects contained in the image with reduced computer processing amount.
The above device extracts a single object as a recognition result. Therefore, if plural methods of recognizing objects are used to increase recognition accuracy, it can be determined whether recognition results agree among the recognition methods. The recognition methods however have no synergistic effect to increase recognition accuracy. Therefore, when recognition results differ among the recognition methods, it is difficult to determine which recognition result is to be adopted.